Computational Method and Simulation of Reliability for Series, Parallel, and k-Out-of-n Systems with Interval Parameters

Abstract

For series, parallel, and k-out-of-n voting system reliability calculation methods, the six σ principles have been proposed in this study to derive the interchange relationship between interval parameters and random parameters. The interval reliability index can be expressed in the function of the random reliability index. The interval reliability index can then be transformed into a random reliability index. The computational method of the reliability for series, parallel, and k-out-of-n voting systems with interval parameters is established. Finally, it has been shown that the proposed method is rational, practical, and applicable with two engineering practical simulations.

Comparison of Structural Probabilistic and Non-Probabilistic Reliability Computational Methods under Big Data Condition

“…The reliability index is 6.025. It can be converted to the interval reliability index of 1.0346 [31]. It is shown that the interval reliability is very close to the industry practices.…”

“…The strength of the welding joint of the U stiffener and roof is also assumed to obey the normal distribution with the mean of 345 MPa and the standard deviation of 15 MPa. In this example, the theoretical lifetime of the part is 204 years, whereas the actual observed lifetime is 165 years [31]. According to the traffic intensity of most intercity highway vehicles, the intensity of amplification λ = 6 is obtained.…”

Reliability Computation of Piezoelectric Actuator Embedded in Flexible Smart Rectangle Cantilever Beam under Complex Gust Load

“…The limit state equation for the mechanical and electrical reliabilities of the piezoelectric cantilever beam under the inverse piezoelectric effect has been derived, and it has been shown that the proposed model is robust and practical with validation by an engineering problem. (15) The reliability of the cantilever beam with interval parameters was calculated by using the first-order second-moment method. (16) Antoniadis et al proposed a novel dynamical adaptive enhanced simulation method coupled with support vector regression.…”